Top Data & Analytics Jobs

Drive and implement TNA IT strategy focusing on customer and employee experiences, leading digital transformation, business platform enhancements, and AI adoption for improved insights and productivity.

Designs and maintains ETL pipelines, data architectures, databases, data models, and master data management solutions. Analyzes, profiles, cleans, and validates complex datasets; establishes data quality standards; and develops automated data management processes. Partners with architects, data stewards, and business stakeholders to improve data integrity and support business requirements. Communicates insights through reports and presentations while using cloud, database, streaming, CI/CD, and data modeling technologies.

Develops dashboards, reports, data models, and measures using data lake and Lakehouse assets. Configures and tests enterprise business systems such as Workday, improves reporting standards, validates data, and documents solutions. Partners with departments to gather requirements, trains reporting and AI champions, promotes self-service analytics, and supports responsible AI use. The role also manages cross-functional requests, identifies data quality issues, and enables teams to independently maintain reporting workflows.

Design and implement mathematical optimization engines and digital‑twin simulation models integrated with predictive ML to optimize production scheduling, sequencing, and resource allocation for aerospace manufacturing facilities. Validate models, analyze large manufacturing datasets, collaborate with ML and plant teams, and translate results for stakeholders to improve throughput and reduce WIP.

Lead the creation and execution of an enterprise Data Governance program: define ownership models, policies, standards, data quality/lineage/security/privacy controls, AI data standards, risk and regulatory reporting oversight, governance KPIs, and select governance tooling while driving cross-functional adoption and senior stakeholder engagement.

Collects and analyzes data, maintains databases, and creates recurring and ad hoc reports, dashboards, graphs, presentations, and documentation. Monitors progress toward business outcomes, supports decision-making, evaluates reporting processes, and recommends workflow and operational improvements. The role also assists with special projects and occasional meeting logistics under direct supervision.

Leads a multidisciplinary team of marketing analysts and researchers, combining marketing analytics, market research, advanced analytics, and performance insights. Oversees data infrastructure, SQL development, pipelines, APIs, ELT/ETL tools, Snowflake, and Tableau dashboards. Guides predictive modeling, segmentation, research methodology, AI experimentation, data governance, and client presentations. Develops team capabilities, workflows, and standards while translating complex data into actionable marketing and business recommendations.

Design, build, and optimize reliable data pipelines and Gold-layer data marts on Databricks Lakehouse in Azure. Develop batch and real-time streaming workflows, dimensional models, data contracts, automated quality tests, governance documentation, monitoring, and CI/CD processes. Collaborate with analytics and data science teams to resolve data issues, improve discoverability, and deliver trusted, high-performance data products.

Designs and optimizes ETL pipelines supporting analytics, data science, and machine learning. Builds labeling, retraining, MLOps, model monitoring, and production inference workflows. Contributes to lakehouse architecture, data modeling, governance, lineage, and quality. Uses infrastructure as code and containerization to create reproducible environments, collaborates with data scientists and BI teams, participates in code reviews, and improves engineering practices.

Supports business application analysis, requirements gathering, process mapping, automation opportunities, documentation, testing, and user acceptance. The analyst translates business needs into functional specifications, user stories, process flows, data models, test cases, and acceptance criteria. Responsibilities also include SQL-based data validation and troubleshooting, application support, issue tracking, stakeholder communication, and collaboration with developers and QA teams.